Explainable Models via Compression of Tree Ensembles. (arXiv:2206.07904v1 [cs.LG])

Web: http://arxiv.org/abs/2206.07904

Ensemble models (bagging and gradient-boosting) of relational decision trees
have proved to be one of the most effective learning methods in the area of
probabilistic logic models (PLMs). While effective, they lose one of the most
important aspect of PLMs -- interpretability. In this paper we consider the
problem of compressing a large set of learned trees into a single explainable
model. To this effect, we propose CoTE -- Compression of Tree Ensembles -- that
produces a single small decision …

arxiv compression lg models tree